Selective Velocity Obstacle Method for Deconflicting Maneuvers Applied to Unmanned Aerial Vehicles

Jenie, Yazdi I.; Kampen, E. van; Visser, Coen C. de; Ellerbroek, Joost; Hoekstra, Joris

doi:10.2514/1.g000737

Cited by 62 publications

(33 citation statements)

References 14 publications

(5 reference statements)

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“…In [22], the authors introduce "the passing on the right rule" to the velocity obstacle method in order to provide a safety rule. In [23], the authors present an extension of [12,22]. In this work, the avoidance rules are designed based on conflict geometry.…”

Section: Related Workmentioning

confidence: 99%

Distributed Cooperative Avoidance Control for Multi-Unmanned Aerial Vehicles

2018

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It is well-known that collision-free control is a crucial issue in the path planning of unmanned aerial vehicles (UAVs). In this paper, we explore the collision avoidance scheme in a multi-UAV system. The research is based on the concept of multi-UAV cooperation combined with information fusion. Utilizing the fused information, the velocity obstacle method is adopted to design a decentralized collision avoidance algorithm. Four case studies are presented for the demonstration of the effectiveness of the proposed method. The first two case studies are to verify if UAVs can avoid a static circular or polygonal shape obstacle. The third case is to verify if a UAV can handle a temporary communication failure. The fourth case is to verify if UAVs can avoid other moving UAVs and static obstacles. Finally, hardware-in-the-loop test is given to further illustrate the effectiveness of the proposed method. IntroductionCurrently, the cooperative control of a multiple unmanned aerial vehicle (UAV) system is attracting growing interest. This is motivated by the constantly growing number of civil and commercial UAV applications [1]. One of the core problems in multi-UAV systems is motion planning, where each UAV navigates a path to the target by sharing each other's information. This should result in a collision-free path derived from UAV motion control. This has driven the development of various UAV collision avoidance algorithms.UAV collision avoidance necessitates a way to predict UAV motion. There are different methods to predict UAV motion and the future positions of the UAV. Early collision avoidance strategies focus on the static obstacles [2,3], using decision trees to avoid various troublesome situations [4], and using path planning to avoid the obstacles [5]. As moving obstacles are more often found in a real environment, many techniques have been proposed for dealing with this situation. For example, in [6], the authors developed a vision-based collision avoidance method by using minimum effort guidance. In [7], the authors presented a reactive avoidance method by using nonlinear differential geometric guidance; in [8,9], the authors developed a local path planning method for UAV navigation; in [10], the authors presented a collision avoidance algorithm by using dynamic programming; in [11], the authors proposed a collision avoidance algorithm based on potential fields; in [12][13][14][15][16][17], the authors presented the velocity obstacle method for deconflicting the UAV paths. Among these methods, the velocity obstacle has been the most actively studied in multi-UAV control over the past ten years. These include methods from avoidance maneuver based on conflict geometry [12]; optimal reciprocal collision avoidance [13]; reciprocal collision avoidance using on-board decentralized sensing without communication [16]; the recursive probabilistic velocity obstacles algorithm [14]; the hybrid reciprocal 1 2 of 25 velocity obstacle algorithm [15]; and the artificial bee colony optimized reciprocal velocity obstacle alg...

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Section: Related Workmentioning

confidence: 99%

Distributed Cooperative Avoidance Control for Multi-Unmanned Aerial Vehicles

2018

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“…An improved VO method was proposed to choose an optimal velocity by introducing a cost function, which consists of a pass time and clearance [9]. A conflict-free maneuver that aims to avoid obstacles while limiting the deviation from the original route was generated by the selective VO method [10]. A finite-time velocity obstacle (FVO) method was used to plan the motion of a mobile robot and complete a crossing through an unknown environment [11].…”

Section: Introductionmentioning

confidence: 99%

Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization

et al. 2020

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An unmanned surface vehicle (USV) plans its global path before the mission starts. When dynamic obstacles appear during sailing, the planned global path must be adjusted locally to avoid collision. This study proposes a local path planning algorithm based on the velocity obstacle (VO) method and modified quantum particle swarm optimization (MQPSO) for USV collision avoidance. The collision avoidance model based on VO not only considers the velocity and course of the USV but also handles the variable velocity and course of an obstacle. According to the collision avoidance model, the USV needs to adjust its velocity and course simultaneously to avoid collision. Due to the kinematic constraints of the USV, the velocity window and course window of the USV are determined by the dynamic window approach (DWA). In summary, local path planning is transformed into a multiobjective optimization problem with multiple constraints in a continuous search space. The optimization problem is to obtain the USV’s optimal velocity variation and course variation to avoid collision and minimize its energy consumption under the rules of the International Regulations for Preventing Collisions at Sea (COLREGs) and the kinematic constraints of the USV. Since USV local path planning is completed in a short time, it is essential that the optimization algorithm can quickly obtain the optimal value. MQPSO is primarily proposed to meet that requirement. In MQPSO, the efficiency of quantum encoding in quantum computing and the optimization ability of representing the motion states of the particles with wave functions to cover the whole feasible solution space are combined. Simulation results show that the proposed algorithm can obtain the optimal values of the benchmark functions and effectively plan a collision-free path for a USV.

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“…Most of the advanced methods for UAVs are using this maneuver to limit the path deviation as small as possible by using, for instance, geometric guidance [42] or optimization of a cost function [43]. Several papers mention this type of maneuver as a deconflict maneuver [53], [71].…”

Section: Types Of Avoidance Maneuvermentioning

confidence: 99%

“…The 'Free-flight' concept introduced in [6], [10], [25], [70], also employs an implicitly coordinated method for avoidance. The work in [19] and [53] presents an example of this type of coordination for UAV applications. This paper also includes methods that only avoid obstacle with known adn constant trajectory, as an implicitly-coordinated avoidance.…”

Section: B Types Of Coordinationmentioning

confidence: 99%

“…The assumption of distributed-dependent surveillance (Sur 2 ), furthermore, reduces the possible uncertainties in the surveillance system and allows the studies to focus more on maneuver optimization. An example of this is presented in [53] that uses the Velocity Obstacle method to generate a deconflicting path with a minimum Closest Point of Approach.…”

Section: Approaches Availabilitymentioning

confidence: 99%

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Taxonomy of Conflict Detection and Resolution Approaches for Unmanned Aerial Vehicle in an Integrated Airspace

Jenie

Kampen

Ellerbroek

et al. 2017

IEEE Trans. Intell. Transport. Syst.

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(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Abstract-This paper proposes a taxonomy of Conflict Detection and Resolution (CD&R) approaches for Unmanned Aerial Vehicles (UAV) operation in an integrated airspace. Possible approaches for UAVs are surveyed and broken down based on their types of surveillance, coordination, maneuver, and autonomy. The factors are combined back selectively, with regard to their feasibility for operation in an integrated airspace, into several 'generic approaches' that form the CD&R taxonomy. These generic approaches are then attributed to a number of available method in the literature to determined their position in the overall CD&R scheme. The attribution shows that many proposed methods are actually unsuitable for operation in an integrated airspace. Furthermore, some part of the taxonomy does not have an adequate representative in the literature, suggesting the need to concentrate UAV CD&R research more in those particular parts. Finally, a multi-layered CD&R architecture is built from the taxonomy, implementing the concept of defense-in-depth to ensure UAVs safe operation in an integrated civil airspace.

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Selective Velocity Obstacle Method for Deconflicting Maneuvers Applied to Unmanned Aerial Vehicles

Cited by 62 publications

References 14 publications

Distributed Cooperative Avoidance Control for Multi-Unmanned Aerial Vehicles

Distributed Cooperative Avoidance Control for Multi-Unmanned Aerial Vehicles

Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization

Taxonomy of Conflict Detection and Resolution Approaches for Unmanned Aerial Vehicle in an Integrated Airspace

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